# devtools::install_github("derekmichaelwright/agData")
library(agData)
library(treemapify)
library(gganimate)
myCaption <- "derekmichaelwright.github.io/dblogr/ | Data: STATCAN"

# Prep data
myColors <- c("darkgreen", "darkorange", "darkred", 
              "antiquewhite4", "steelblue", "darkblue")
myMeasures <- c("Area Seeded", "Area Harvested", "Production", 
                "Yield", "Average Farm Price", "Total Farm Value")
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan") %>%
  mutate(Measurement = factor(Measurement, levels = myMeasures),
         Value = ifelse(Measurement != "Yield",
                        Value / 1000000, Value / 1000),
         Unit = plyr::mapvalues(Unit, c("Hectares", "Tonnes", "kg/ha"), 
                  c("Million Hectares", "Million Tonnes", "Tonnes/ Hectare")))
myCrops <- unique(xx$Crop)
# Plot
pdf("figures_crops_saskatchewan_statcan.pdf", width = 10, height = 6)
for(i in myCrops) {
  xi <- xx %>% filter(Crop == i)
  print(ggplot(xi, aes(x = Year, y = Value, color = Measurement)) +
    geom_line(size = 1.5, alpha = 0.7) +
    facet_wrap(. ~ Measurement, scales = "free_y", ncol = 3) +
    scale_color_manual(values = myColors) +
    scale_x_continuous(breaks = seq(1920, 2020, by = 20) ) +
    theme_agData(legend.position = "none", 
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = i, y = NULL, x = NULL, caption = myCaption) )
}
dev.off()

# Create function to determine top crops
cropList <- function(measurement, years) {
  # Prep data
  xx <- agData_STATCAN_Crops %>% 
    filter(Area == "Saskatchewan", Measurement == measurement, Year %in% years) 
  # Get top 15 crops from each year
  topcrops <- function(x, year) {
    x <- x %>% filter(Year == year) %>% arrange(desc(Value)) %>% 
      pull(Crop) %>% unique() %>% as.character()
  }
  myCrops <- NULL
  for(i in years) { myCrops <- c(myCrops, topcrops(xx, i)) }
  unique(myCrops)
}

# Prep data
myCrops <- cropList(measurement = "Production", years = 2020)[c(1:8)]
myCrops <- c(myCrops, "Other")
x1 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan",
         Measurement == "Production", 
         Crop %in% myCrops) 
x2 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan",
         Measurement == "Production", 
         !Crop %in% myCrops) %>%
  group_by(Year) %>%
  summarise(Value = sum(Value, na.rm = T)) %>%
  ungroup() %>%
  mutate(Crop = "Other")
xx <- bind_rows(x1, x2) %>%
  mutate(Crop = factor(Crop, levels = rev(myCrops)))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000000, fill = Crop)) + 
  geom_bar(stat = "identity", alpha = 0.7, color = "black", lwd = 0.1) + 
  scale_fill_manual(name = NULL, values = agData_Colors[c(9:1)]) +
  scale_x_continuous(breaks = seq(1910, 2020, by=10)) +
  guides(fill = guide_legend(override.aes = list(lwd = 0.4))) +
  theme_agData() + 
  labs(title = "Saskatchewan - Crop Production", 
       y = "Million Tonnes", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_04.png", mp, width = 6, height = 4)

# Prep data
xx <- xx %>% filter(Year %in% c(2020, 2021)) %>%
  mutate(Crop = factor(Crop, levels = myCrops),
         Year = factor(Year))
# Plot
mp <- ggplot(xx, aes(x = Crop, y = Value / 1000000, 
                     fill = Crop, alpha = Year)) + 
  geom_bar(stat = "identity", position = "dodge", color = "black") + 
  scale_fill_manual(values = agData_Colors, guide = F) +
  scale_alpha_manual(name = NULL, values = c(0.7, 0.3)) +
  theme_agData(legend.position = "bottom") + 
  labs(title = "Saskatchewan - Crop Production - 2020 vs. 2021", 
       y = "Million Tonnes", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_05.png", mp, width = 6, height = 4)

# Prep data
myCrops <- cropList(measurement = "Production", years = 2020)[c(1:8)]
myCrops <- c(myCrops, "Other")
myYears <- c(1935,1936,1937,1938,1939, 1952,1953,1954,1955,1956, 
             1959,1960,1961,1962,1963, 1986,1987,1988,1989,1990, 
             2000,2001,2002,2003,2004, 2019,2020,2021,2022,2023)
myGroups <- c(rep(1:6, each = 5))
x1 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan",
         Measurement == "Production", 
         Crop %in% myCrops) 
x2 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan",
         Measurement == "Production", 
         !Crop %in% myCrops) %>%
  group_by(Year) %>%
  summarise(Value = sum(Value, na.rm = T)) %>%
  ungroup() %>%
  mutate(Crop = "Other")
xx <- bind_rows(x1, x2) %>%
  filter(Year %in% myYears) %>%
  mutate(Crop = factor(Crop, levels = rev(myCrops)),
         Group = plyr::mapvalues(Year, myYears, myGroups))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000000, fill = Crop)) + 
  geom_bar(stat = "identity", alpha = 0.7, color = "black", lwd = 0.3) + 
  facet_wrap(Group ~ . , ncol = 6, scales = "free") +
  scale_fill_manual(name = NULL, values = agData_Colors[c(9:1)]) +
  guides(fill = guide_legend(override.aes = list(lwd = 0.4))) +
  theme_agData(axis.text.x = element_text(angle = 45, hjust = 1),
               strip.text = element_blank()) + 
  labs(title = "Saskatchewan - Drought Years", x = NULL,
       y = "Million Tonnes", caption = myCaption)
ggsave("crops_saskatchewan_06.png", mp, width = 8, height = 4)

# Prep data
myCrops <- cropList(measurement = "Yield", years = 2020)[-1]
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Year == 2020,
         Measurement == "Yield", Crop %in% myCrops) %>%
  mutate(Crop = factor(Crop, levels = myCrops) )
# Plot
mp <- ggplot(xx, aes(x = Crop, y = Value / 1000, fill = Crop)) + 
  geom_bar(stat = "identity", color = "Black", alpha = 0.7) + 
  facet_grid(Year ~ .) + 
  scale_fill_manual(values = agData_Colors) +
  theme_agData(legend.position = "none", 
               axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(title = "Saskatchewan - Crop yields", 
       y = "Tonnes / Hectare", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_07.png", mp, width = 6, height = 5)

# Prep data
pp <- agData_STATCAN_Population %>% 
  filter(Month == 1, Area == "Saskatchewan", Year == 2020) %>%
  pull(Value)
myCrops <- cropList(measurement = "Production", years = 2020)
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Year %in% 2020,
         Measurement == "Production", Crop %in% myCrops) %>%
  mutate(Crop = factor(Crop, levels = myCrops),
         PerPerson = Value / pp) %>%
  filter(PerPerson > 0.1)
# Plot
mp <- ggplot(xx, aes(x = Crop, y = PerPerson, fill = Crop)) + 
  geom_bar(stat = "identity", color = "Black", alpha = 0.7) + 
  geom_label(aes(label = round(PerPerson,1)), vjust = -0.15, fill = "White",
             size = 3, label.padding = unit(0.15, "lines")) +
  facet_grid(Year~.) + 
  scale_y_continuous(limits = c(0,15), expand = c(0,0)) +
  scale_fill_manual(values = agData_Colors) +
  theme_agData(legend.position = "none", 
               axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(title = "Saskatchewan - Crop Production Per Person", 
       y = "1000 kg", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_08.png", mp, width = 6, height = 4)

# Prep data
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Year == 2020, 
         Measurement == "Area Seeded") %>%
  arrange(desc(Value)) %>%
  mutate(Crop = factor(Crop, levels = unique(.$Crop)))
# Plot
mp <- ggplot(xx, aes(area = Value, fill = Crop, label = Crop)) +
  geom_treemap(color = "black", alpha = 0.7, size = 1.5) +
  geom_treemap_text(place = "centre", grow = T, color = "white") +
  scale_fill_manual(values = agData_Colors) +
  theme_agData(legend.position = "none") +
  labs(title = "Saskatchewan Cropland", 
       subtitle = "Area Seeded 2020", caption = myCaption)
ggsave("crops_saskatchewan_11.png", mp, width = 6, height = 4)

# Prep data
xx <- agData_STATCAN_FarmLand_Use %>% 
  filter(Area == "Saskatchewan", Item == "Total area of farms", 
         Unit == "Hectares", !is.na(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000000)) + 
  geom_bar(stat = "identity", alpha = 0) +
  geom_line(color = "darkgreen", size = 1.25, alpha = 0.7) +
  scale_x_continuous(breaks = seq(1920, 2020, 10)) +
  scale_y_continuous(breaks = seq(0, 30, 5)) +
  theme_agData() +
  labs(title = "Saskatchewan - Total Farmland Area", 
       y = "Million Hectares", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_12.png", mp, width = 6, height = 4)

# Prep data
myColors <- c("darkgreen","darkgoldenrod2","darkred")
myCrops <- c("Summerfallow", "Wheat", "Canola")
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Measurement == "Area Seeded",
         Crop %in% myCrops) %>%
  mutate(Crop = factor(Crop, levels = myCrops))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000000, 
                     color = Crop, alpha = Crop, size = Crop)) + 
  geom_line() +
  scale_color_manual(name = NULL, values = myColors) +
  scale_alpha_manual(name = NULL, values = c(0.8,0.6,0.6)) +
  scale_size_manual(name = NULL, values = c(1.75,1.5,1.5)) +
  scale_x_continuous(breaks = seq(1910, 2020, 10)) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Saskatchewan - Summerfallow", 
       y = "Million Hectares", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_13.png", mp, width = 6, height = 4)

# Prep data
myCrops <- cropList(measurement = "Area Seeded", years = c(1970, 2020))
xx <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Year %in% c(1970,2020),
         Measurement == "Area Seeded") %>%
  arrange(desc(Value)) %>%
  mutate(Crop = factor(Crop, levels = unique(.$Crop)))
# Plot
mp <- ggplot(xx, aes(x = "", y = Value / 1000000, fill = Crop)) + 
  geom_bar(stat = "identity", color = "black") +
  facet_grid(. ~ Year) +
  scale_fill_manual(name = NULL, values = agData_Colors) +
  #scale_alpha_manual(name = NULL, values = c(0.8,0.6,0.6)) +
  #scale_size_manual(name = NULL, values = c(1.75,1.5,1.5)) +
  #scale_x_continuous(breaks = seq(1910, 2020, 10)) +
  theme_agData() +
  labs(title = "Saskatchewan - Summerfallow", 
       y = "Million Hectares", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_14.png", mp, width = 6, height = 4)
levels(xx$Crop)

# Prep data
myColors <- c("darkgoldenrod2","darkgreen","darkred")
x1 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Measurement == "Production",
         Crop %in% c("Lentils", "Peas", "Beans", "Chickpeas")) %>%
  group_by(Year) %>% 
  summarise(Value = sum(Value)) %>%
  mutate(Crop = "Fake Meat Proteins (Legumes)")
x2 <- agData_STATCAN_Crops %>% 
  filter(Area == "Saskatchewan", Measurement == "Production",
         Crop %in% c("Canola", "Wheat")) %>%
  mutate(Crop = plyr::mapvalues(Crop, c("Canola", "Wheat"), 
                                      c("Evil GMO Oilseeds (Canola)", "Toxic Gluten (Wheat)")))
xx <- bind_rows(x1, x2) %>% filter(Year < 2021) %>%
  mutate(Crop = factor(Crop, levels = c("Toxic Gluten (Wheat)", "Fake Meat Proteins (Legumes)", 
                                        "Evil GMO Oilseeds (Canola)")))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000000, color = Crop)) +
  geom_line(size = 1.5, alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(breaks = seq(1910, 2020, 10)) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Saskatchewan's Industrialized Food Production", 
       y = "Million Tonnes", x = NULL, caption = myCaption)
ggsave("crops_saskatchewan_15.png", mp, width = 6.5, height = 4)